CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency.To improve the overall performance of the CR systemit ...CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency.To improve the overall performance of the CR systemit is extremely important to adapt or reconfigure the systemparameters.The Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter adaptation.As meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches,the performance of these algorithms is investigated in order to design an efficient CR system that is able to adapt the transmitting parameters to effectively reduce power consumption,bit error rate and adjacent interference of the channel,while maximized secondary user throughput.Self-Learning Salp Swarm Algorithm(SLSSA)is a recent meta-heuristic algorithm that is the enhanced version of SSA inspired by the swarming behavior of salps.In this work,the parametric adaption of CR system is performed by SLSSA and the simulation results show that SLSSA has high accuracy,stability and outperforms other competitive algorithms formaximizing the throughput of secondary users.The results obtained with SLSSA are also shown to be extremely satisfactory and need fewer iterations to converge compared to the competitive methods.展开更多
The rapid emergence of novel virus named SARS-CoV2 and unchecked dissemination of this virus around the world ever since its outbreak in 2020,provide critical research criteria to assess the vulnerabilities of our cur...The rapid emergence of novel virus named SARS-CoV2 and unchecked dissemination of this virus around the world ever since its outbreak in 2020,provide critical research criteria to assess the vulnerabilities of our current health system.The paper addresses our preparedness for the management of such acute health emergencies and the need to enhance awareness,about public health and healthcare mechanisms.In view of this unprecedented health crisis,distributed ledger and AI technology can be seen as one of the promising alternatives for fighting against such epidemics at the early stages,and with the higher efficacy.At the implementation level,blockchain integration,early detection and avoidance of an outbreak,identity protection and safety,and a secure drug supply chain can be realized.At the opposite end of the continuum,artificial intelligence methods are used to detect corona effects until they become too serious,avoiding costly drug processing.The paper explores the application of blockchain and artificial intelligence in order to fight with COVID-19 epidemic scenarios.This paper analyzes all possible newly emerging cases that are employing these two technologies for combating a pandemic like COVID-19 along with major challenges which cover all technological and motivational factors.This paper has also discusses the potential challenges and whether further production is required to establish a health monitoring system.展开更多
Remote sensing image processing engaged researchers’attentiveness in recent years,especially classification.The main problem in classification is the ratio of the correct predictions after training.Feature extraction...Remote sensing image processing engaged researchers’attentiveness in recent years,especially classification.The main problem in classification is the ratio of the correct predictions after training.Feature extraction is the foremost important step to build high-performance image classifiers.The convolution neural networks can extract images’features that significantly improve the image classifiers’accuracy.This paper proposes two efficient approaches for remote sensing images classification that utilizes the concatenation of two convolution channels’outputs as a features extraction using two classic convolution models;these convolution models are the ResNet 50 and the DenseNet 169.These elicited features have been used by the fully connected neural network classifier and support vector machine classifier as input features.The results of the proposed methods are compared with other antecedent approaches in the same experimental environments.Evaluation is based on learning curves plotted during the training of the proposed classifier that is based on a fully connected neural network and measuring the overall accuracy for the both proposed classifiers.The proposed classifiers are used with their trained weights to predict a big remote sensing scene’s classes for a developed test.Experimental results ensure that,compared with the other traditional classifiers,the proposed classifiers are further accurate.展开更多
Segmentation of vessel in retinal fundus images is a primary step for the clinical identification for specific eye diseases.Effective diagnosis of vascular pathologies from angiographic images is thus a vital aspect a...Segmentation of vessel in retinal fundus images is a primary step for the clinical identification for specific eye diseases.Effective diagnosis of vascular pathologies from angiographic images is thus a vital aspect and generally depends on segmentation of vascular structure.Although various approaches for retinal vessel segmentation are extensively utilized,however,the responses are lower at vessel’s edges.The curvelet transform signifies edges better than wavelets,and hence convenient for multiscale edge enhancement.The bilateral filter is a nonlinear filter that is capable of providing effective smoothing while preserving strong edges.Fast bilateral filter is an advanced version of bilateral filter that regulates the contrast while preserving the edges.Therefore,in this paper a fusion algorithm is recommended by fusing fast bilateral filter that can effectively preserve the edge details and curvelet transform that has better capability to detect the edge direction feature and better investigation and tracking of significant characteristics of the image.Afterwards C mean thresholding is used for the extraction of vessel.The recommended fusion approach is assessed on DRIVE dataset.Experimental results illustrate that the fusion algorithm preserved the advantages of the both and provides better result.The results demonstrate that the recommended method outperforms the traditional approaches.展开更多
For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but faul...For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but fault tolerance and energy balancing gives equal importance for improving the network lifetime.For saving energy in WSNs,clustering is considered as one of the effective methods for Wireless Sensor Networks.Because of the excessive overload,more energy consumed by cluster heads(CHs)in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to failure.For increasing the WSNs’lifetime,the CHs selection has played a key role in energy consumption for sensor nodes.An Energy Efficient Unequal Fault Tolerant Clustering Approach(EEUFTC)is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization(PSO).In this approach,an optimal Master Cluster Head(MCH)-Master data Aggregator(MDA),selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator(MDA),and Master Cluster Head.The data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the BS.Thus,the MCH overhead reduces.During the heavy communication of data,overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA).To describe the proposed method superiority based on various performance metrics,simulation and results are compared to the existing methods.展开更多
The trend of digital information transformation has become a topic of interest.Many data are threatening;thus,protecting such data from attackers is considered an essential process.Recently,a new methodology for data ...The trend of digital information transformation has become a topic of interest.Many data are threatening;thus,protecting such data from attackers is considered an essential process.Recently,a new methodology for data concealing has been suggested by researchers called coverless steganography.Coverless steganography can be accomplished either by building an image database to match its image subblocks with the secret message to obtain the stego image or by generating an image.This paper proposes a coverless image steganography system based on pure image generation using secret message bits with a capacity higher than the other traditional systems.The system uses the secret message to generate the stego image in the form of one of the Intelligence Quotient(IQ)games,the maze.Firstly,a full grid is generated with several specific rows and columns determined from the number of bits of the secret message.Then,these bits are fed to the full grid to form the maze game stego image.Finally,the generated maze game stego image is sent to the recipient.The experimental results,using the Bit Error Rate(BER),were conducted,and confirmed the strength of this system represented by a high capacity,perfect performance,robustness,and stronger hiding system compared with existing coverless steganography systems.展开更多
基金The authors would like to thank for the support from Taif University Researchers Supporting Project Number(TURSP-2020/239),Taif University,Taif,Saudi Arabia。
文摘CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency.To improve the overall performance of the CR systemit is extremely important to adapt or reconfigure the systemparameters.The Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter adaptation.As meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches,the performance of these algorithms is investigated in order to design an efficient CR system that is able to adapt the transmitting parameters to effectively reduce power consumption,bit error rate and adjacent interference of the channel,while maximized secondary user throughput.Self-Learning Salp Swarm Algorithm(SLSSA)is a recent meta-heuristic algorithm that is the enhanced version of SSA inspired by the swarming behavior of salps.In this work,the parametric adaption of CR system is performed by SLSSA and the simulation results show that SLSSA has high accuracy,stability and outperforms other competitive algorithms formaximizing the throughput of secondary users.The results obtained with SLSSA are also shown to be extremely satisfactory and need fewer iterations to converge compared to the competitive methods.
基金funded by the Taif University Researchers Supporting Projects at Taif University,Kingdom of Saudi Arabia,under grant number:TURSP-2020/239.
文摘The rapid emergence of novel virus named SARS-CoV2 and unchecked dissemination of this virus around the world ever since its outbreak in 2020,provide critical research criteria to assess the vulnerabilities of our current health system.The paper addresses our preparedness for the management of such acute health emergencies and the need to enhance awareness,about public health and healthcare mechanisms.In view of this unprecedented health crisis,distributed ledger and AI technology can be seen as one of the promising alternatives for fighting against such epidemics at the early stages,and with the higher efficacy.At the implementation level,blockchain integration,early detection and avoidance of an outbreak,identity protection and safety,and a secure drug supply chain can be realized.At the opposite end of the continuum,artificial intelligence methods are used to detect corona effects until they become too serious,avoiding costly drug processing.The paper explores the application of blockchain and artificial intelligence in order to fight with COVID-19 epidemic scenarios.This paper analyzes all possible newly emerging cases that are employing these two technologies for combating a pandemic like COVID-19 along with major challenges which cover all technological and motivational factors.This paper has also discusses the potential challenges and whether further production is required to establish a health monitoring system.
基金The authors would like to thank the Deanship of Scientific Research,Taif University Researchers Supporting Project Number(TURSP-2020/239),Taif University,Taif,Saudi Arabia for supporting this research work.
文摘Remote sensing image processing engaged researchers’attentiveness in recent years,especially classification.The main problem in classification is the ratio of the correct predictions after training.Feature extraction is the foremost important step to build high-performance image classifiers.The convolution neural networks can extract images’features that significantly improve the image classifiers’accuracy.This paper proposes two efficient approaches for remote sensing images classification that utilizes the concatenation of two convolution channels’outputs as a features extraction using two classic convolution models;these convolution models are the ResNet 50 and the DenseNet 169.These elicited features have been used by the fully connected neural network classifier and support vector machine classifier as input features.The results of the proposed methods are compared with other antecedent approaches in the same experimental environments.Evaluation is based on learning curves plotted during the training of the proposed classifier that is based on a fully connected neural network and measuring the overall accuracy for the both proposed classifiers.The proposed classifiers are used with their trained weights to predict a big remote sensing scene’s classes for a developed test.Experimental results ensure that,compared with the other traditional classifiers,the proposed classifiers are further accurate.
基金The authors would like to thank for the support from Taif University Researchers Supporting Project number(TURSP-2020/239),Taif University,Taif,Saudi Arabia.
文摘Segmentation of vessel in retinal fundus images is a primary step for the clinical identification for specific eye diseases.Effective diagnosis of vascular pathologies from angiographic images is thus a vital aspect and generally depends on segmentation of vascular structure.Although various approaches for retinal vessel segmentation are extensively utilized,however,the responses are lower at vessel’s edges.The curvelet transform signifies edges better than wavelets,and hence convenient for multiscale edge enhancement.The bilateral filter is a nonlinear filter that is capable of providing effective smoothing while preserving strong edges.Fast bilateral filter is an advanced version of bilateral filter that regulates the contrast while preserving the edges.Therefore,in this paper a fusion algorithm is recommended by fusing fast bilateral filter that can effectively preserve the edge details and curvelet transform that has better capability to detect the edge direction feature and better investigation and tracking of significant characteristics of the image.Afterwards C mean thresholding is used for the extraction of vessel.The recommended fusion approach is assessed on DRIVE dataset.Experimental results illustrate that the fusion algorithm preserved the advantages of the both and provides better result.The results demonstrate that the recommended method outperforms the traditional approaches.
基金The authors would like to thank for the support from Taif University Researchers Supporting Project number(TURSP-2020/239),Taif University,Taif,Saudi Arabia.
文摘For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but fault tolerance and energy balancing gives equal importance for improving the network lifetime.For saving energy in WSNs,clustering is considered as one of the effective methods for Wireless Sensor Networks.Because of the excessive overload,more energy consumed by cluster heads(CHs)in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to failure.For increasing the WSNs’lifetime,the CHs selection has played a key role in energy consumption for sensor nodes.An Energy Efficient Unequal Fault Tolerant Clustering Approach(EEUFTC)is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization(PSO).In this approach,an optimal Master Cluster Head(MCH)-Master data Aggregator(MDA),selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator(MDA),and Master Cluster Head.The data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the BS.Thus,the MCH overhead reduces.During the heavy communication of data,overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA).To describe the proposed method superiority based on various performance metrics,simulation and results are compared to the existing methods.
基金Taif University Researchers Supporting Project Number(TURSP-2020/239),Taif University,Taif,Saudi Arabia.
文摘The trend of digital information transformation has become a topic of interest.Many data are threatening;thus,protecting such data from attackers is considered an essential process.Recently,a new methodology for data concealing has been suggested by researchers called coverless steganography.Coverless steganography can be accomplished either by building an image database to match its image subblocks with the secret message to obtain the stego image or by generating an image.This paper proposes a coverless image steganography system based on pure image generation using secret message bits with a capacity higher than the other traditional systems.The system uses the secret message to generate the stego image in the form of one of the Intelligence Quotient(IQ)games,the maze.Firstly,a full grid is generated with several specific rows and columns determined from the number of bits of the secret message.Then,these bits are fed to the full grid to form the maze game stego image.Finally,the generated maze game stego image is sent to the recipient.The experimental results,using the Bit Error Rate(BER),were conducted,and confirmed the strength of this system represented by a high capacity,perfect performance,robustness,and stronger hiding system compared with existing coverless steganography systems.
